Various Channel Estimation Techniques for OFDM Systems through Different Parameters
D. Vimala1, P. Nandhini2, R. Elankavi3
1D. Vimala, Department of CSE, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
2P. Nandhini, Department of CSE, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
3R. Elankavi, Department of CSE, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
Manuscript received on 11 October 2019 | Revised Manuscript received on 25 October 2019 | Manuscript Published on 26 December 2019 | PP: 999-1003 | Volume-8 Issue-12S October 2019 | Retrieval Number: K127610812S19/2019©BEIESP | DOI: 10.35940/ijitee.K1276.10812S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Channel estimation in OFDM system can be performed in many ways, channel estimation is the major essential component to be calculated for the wireless communication systems (such as mobile) because these are very much effective to noise than compared to the wired communications (landlines).it has loss of data due to noise, multipath degradation, fading, etc .channel estimation is the crucial task for the effective and the reliable wireless transmission. These OFDM based systems can be improved by the pilot assisted channel estimation .As compared with the various techniques we are going to conclude that which of the available techniques are more efficient to achieve the great results. Various channel estimation techniques involved in it they are LS, LMS, RLS, MMSE, NLMS, LMMS, WLs, Sparse. It may includes the complexity of building ,effective to the noise, efficiency and comparing with many factors and probability of acquisition versus number of users are analyzed using QAM. Channel frequency response versus carrier no. and Error value versus the Sample performance after analyzing all the performance parameters we will conclude the best compared to other in all aspects. Sparse will have better performance as compared with the other six remaining channel estimations.
Keywords: Least Square(LS) , Least Mean Square (LMS), Recursive Least Square Error (RLS), Normalized Least Mean Square (NLMS) , Minimum Mean Square (MMS), Linear Minimum Mean Square(LMMS), Weighted Least Square (WLS), Sparse, Quadrate Amplitude Modulation (QAM).
Scope of the Article: Expert Systems